Knowledge / Software & AI

Software & AI · Technique choice · Written and maintained by Haink’s AI adoption team · Updated August 2026 · 8 min read

RAG vs Fine-Tuning: Which Should You Use?

RAG and fine-tuning solve different problems and are often used together. In short: use RAG to give a model knowledge, and fine-tuning to give it behavior. RAG retrieves relevant information at query time so answers stay accurate and current; fine-tuning adjusts the model's weights so it learns a tone, format or skill. The most common and expensive mistake is reaching for fine-tuning when retrieval would have been cheaper, faster and more accurate.

What retrieval-augmented generation (RAG) does

RAG retrieves relevant information from your documents or databases at query time and feeds it to the model as context. It is the right tool when the model needs access to private, large, or frequently-changing knowledge. Because answers are grounded in retrieved sources, RAG reduces hallucination and lets you cite where an answer came from — and you update knowledge by updating documents, with no retraining.

What fine-tuning does

Fine-tuning continues training a model on your examples so it learns a behaviour, format or style. It is the right tool for a consistent tone, a strict output structure, a specialised classification task, or a domain skill that cannot be expressed as retrieved context.

The distinction that makes the rest of this page predictable is narrower than “knowledge versus behaviour”: fine-tuning changes the distribution the model samples from; retrieval changes what is in its context window. One shifts likelihood, the other performs lookup.

Which explains the failure mode teams actually hit, and it is not the one they brace for. A fine-tuned model does not go quiet on facts it was not taught. It produces a plausible, confidently wrong fact in exactly the right format — because you taught it the shape of your answers, not their content, and the correct shape is precisely what makes the wrong answer hard to spot in review. A generic model that answers oddly gets checked. A fine-tuned one that answers in house style does not.

The ladder, not the binary

Posing this as RAG against fine-tuning hides three other options, two of which are usually the right answer. Ordered by what they cost to reach:

Distillation is the rung nobody names, and it is what people usually mean when they say fine-tuning cut their inference bill. You use a large model to generate high-quality outputs on your specific task, then fine-tune a small model on those outputs. The small model does not become generally smarter — it becomes specifically good at your one job, at a fraction of the per-token cost. Fine-tuning did not become cheap; it let you stop paying frontier prices for a narrow task.

RAG vs fine-tuning side by side

When to use which

• Need current or private knowledge? Use RAG.
• Need a specific output format, tone or behavior? Fine-tune.
• Worried about hallucination and traceability? RAG, because it grounds and cites.
• Have a narrow, repetitive classification task with good labels? Fine-tuning can be efficient and cheap to run.
• Need both knowledge and behavior? Combine them.

Using both together

The two are complementary. A common production pattern is RAG for knowledge plus light fine-tuning for tone or output structure — for example, a support assistant that retrieves the right policy (RAG) and always answers in your brand voice and a fixed JSON schema (fine-tuning). Start with RAG, prove value, and add fine-tuning once you have real usage data to fine-tune on.

When fine-tuning pays for itself

The case for distillation is a cost case, so it should be argued with a number rather than a preference. It has the shape every make-or-buy decision has: a fixed cost up front, a smaller marginal cost afterwards, and a volume at which the second beats the first.

Take the saving first. Replacing a frontier-tier model at roughly $3.64 per million tokens blended with a small model at roughly $0.153 saves about $3.49 per million tokens. Now the fixed cost — dominated by data curation rather than compute, and landing in the same band as any focused custom build:

Two things about that number are worth sitting with. First, it is roughly the same order of magnitude as the break-even for owning the hardware instead of renting inference — around ten billion tokens a year in both cases. Owning the model and owning the machine become worthwhile at about the same scale, which is a useful coincidence: below it, rent both; above it, the two decisions arrive together and should be taken together.

Second, the whole calculation assumes the small model actually matches quality on your task. If it does not, the saving is fictional and the correct comparison is not cost at all. Which is why the honest sequence is: prove the task works with prompting and retrieval on a large model, measure real volume for a quarter, and only then ask whether distilling it down is worth the fixed cost. Doing it in the other order — distilling first because inference looks expensive — is how teams end up with a cheap model that is cheap because it is worse.

RAG, by contrast, has moderate upfront effort and near-zero cost to change: you edit a document. That asymmetry, not accuracy, is why it belongs first in almost every project. Cost ranges for both are in how much custom AI costs.

Common misconceptions

• “Fine-tuning will teach the model our data.” Not reliably — for facts, use retrieval.
• “RAG is just a vector search.” Production RAG also needs chunking, hybrid retrieval, re-ranking and evaluation.
• “We must choose one.” The best systems often use both for different jobs.
• “Fine-tuning will make it cheaper.” Only through distillation, only on a narrow task, and only above roughly ten billion tokens a year. Below that the fixed cost never amortises.
• “A fine-tuned model will admit what it doesn’t know.” The opposite is the common failure: it returns a wrong fact in the right format, which is harder to catch in review than an obviously odd answer.

Related Resources

• LLM Applications & RAG
• How to Build a Production RAG System
• Software & AI Development Services
• How Much Does Custom AI Cost?
• On-Premises vs Cloud LLM — the other break-even that lands at the same scale
• How AI Document Processing Works — where neither technique is the answer and rules are

Frequently Asked Questions

What is the difference between RAG and fine-tuning?

RAG retrieves relevant information at query time and feeds it to the model as context — best for private or changing knowledge. Fine-tuning adjusts the model's weights to teach a behavior, format or style. RAG gives knowledge; fine-tuning gives behavior. They solve different problems and are often combined.

Should I use RAG or fine-tuning?

Start with RAG for anything that needs access to your knowledge, because it is cheaper, easier to update and reduces hallucination. Add fine-tuning when you need a specific behavior or output format that prompting and retrieval can't achieve.

Does fine-tuning teach the model new facts?

Not reliably. Fine-tuning is good for behavior, tone and format; for factual, current or private knowledge, retrieval-augmented generation is the better approach.

Can you use RAG and fine-tuning together?

Yes — a common production pattern is RAG for knowledge plus light fine-tuning for tone or output structure, such as a support bot that retrieves the right policy and answers in a fixed voice and format.

Is fine-tuning more expensive than RAG?

Upfront, almost always — the cost is dominated by curating and labelling training data, not by the training run, and changing it later means retraining rather than editing a document. Fine-tuning can lower per-request cost by letting a smaller model do a narrow job, but that only pays back on volume: at a $50,000 fixed cost and a saving of about $3.49 per million tokens, break-even is near 14 billion tokens a year. Below that you are paying to build something you could have rented for less.

What is distillation, and how is it different from fine-tuning?

Distillation is fine-tuning with a specific purpose: you use a large model to generate high-quality outputs on your task, then train a small model on those outputs so it can do that one job at a fraction of the per-token cost. It is what people usually mean when they say fine-tuning reduced their inference bill. The small model does not become generally smarter and cannot exceed the model it learned from — it becomes specifically good at your narrow task.

How much training data does fine-tuning need?

It depends on what is being taught, and any single number is a guess. Tone and output format with a LoRA adapter can move on a few hundred well-chosen examples; a genuine classification skill usually wants thousands, with label quality mattering more than raw count. The cost is rarely the training run — it is the human time to curate, label and clean the examples, which is the line most first budgets leave out.